File size: 7,530 Bytes
e8055cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | """Tests for harness/A/run.py -- question loading, scoring, and result-file writing."""
import json
import pytest
from harness import A
from harness.A import run as harness_run
_FAKE_ANSWER = {
"prompt_text": "<rendered chat template>",
"answer_text": "4",
"answer_raw": "<|im_start|>assistant\n4<|im_end|>",
"input_token_count": 123,
"vision_input_shapes": {"pixel_values": [512, 1536]},
"output_token_ids": [19, 151645],
"output_token_count": 2,
"hit_token_limit": False,
"eos_token_ids": [151645],
"generation_seconds": 1.234,
"device": "cuda",
"dtype": "bfloat16",
"library_versions": {"transformers": "5.14.1", "torch": "2.13.0+cu130"},
"generation_config": {
"max_new_tokens": 16,
"do_sample": False,
"temperature": 0.0,
"top_p": None,
"top_k": None,
},
}
_FAKE_ROW = {
"id": 7,
"scene_name": "scene0001_00",
"dataset": "scannet",
"question_type": "object_counting",
"question": "How many chairs?",
"options": None,
"ground_truth": "4",
}
_FAKE_FRAME_INFO = {
"protocol": "base",
"video_path": "/root/data/VSI-Bench/scannet/scene0001_00.mp4",
"frame_timestamps": [0.0, 1.0, 2.0],
"frame_indices": [0, 30, 60],
"frame_selection": "uniform",
"frame_count": 16,
}
def test_load_questions_reads_every_row(tmp_path):
jsonl = tmp_path / "test.jsonl"
jsonl.write_text(
"\n".join(
json.dumps({"id": i, "scene_name": f"scene{i}", "question": "q"})
for i in range(3)
)
)
rows = harness_run.load_questions(jsonl)
assert [r["id"] for r in rows] == [0, 1, 2]
def test_load_questions_filters_by_scene(tmp_path):
jsonl = tmp_path / "test.jsonl"
jsonl.write_text(
"\n".join(
json.dumps({"id": i, "scene_name": "a" if i < 2 else "b", "question": "q"})
for i in range(4)
)
)
rows = harness_run.load_questions(jsonl, scene="b")
assert [r["id"] for r in rows] == [2, 3]
def test_load_questions_respects_limit(tmp_path):
jsonl = tmp_path / "test.jsonl"
jsonl.write_text(
"\n".join(
json.dumps({"id": i, "scene_name": "a", "question": "q"}) for i in range(5)
)
)
rows = harness_run.load_questions(jsonl, limit=2)
assert [r["id"] for r in rows] == [0, 1]
def test_scalar_score_returns_metric_name_and_value():
doc = {"question_type": "object_counting", "ground_truth": "4"}
score_doc = harness_run.vsi_official_eval.vsibench_process_results(doc, ["4"])[
"vsibench_score"
]
metric_name, value = harness_run._scalar_score("object_counting", score_doc)
assert metric_name == "MRA:.5:.95:.05"
assert value == 1.0
def test_scalar_score_rejects_unknown_question_type():
with pytest.raises(ValueError):
harness_run._scalar_score("not_a_real_type", {})
def test_results_dir_for_matches_established_dimension_nesting():
root = harness_run.results_dir_for("qwen3.5-4b", "base", "selective", 32)
assert root == A.RESULTS_DIR / "qwen3.5-4b" / "selective" / "32"
def test_results_dir_for_keeps_protocols_together():
base = harness_run.results_dir_for("qwen3.5-4b", "base", "selective", 32)
extended = harness_run.results_dir_for("qwen3.5-4b", "thinking", "selective", 32)
assert base == extended
def test_results_dir_for_honors_explicit_override(tmp_path):
assert (
harness_run.results_dir_for("qwen3.5-4b", "base", "uniform", 16, tmp_path)
== tmp_path
)
def test_build_record_preserves_every_field_untruncated():
record = harness_run._build_record(
_FAKE_ROW,
"full prompt text",
_FAKE_ANSWER,
"MRA:.5:.95:.05",
1.0,
"qwen3.5-4b",
"/root/models/qwen3.5-4b",
_FAKE_FRAME_INFO,
)
assert record["question"] == "How many chairs?"
assert record["full_prompt"] == "full prompt text"
assert record["rendered_prompt"] == _FAKE_ANSWER["prompt_text"]
assert record["answer_given"] == "4"
assert record["answer_raw"] == _FAKE_ANSWER["answer_raw"]
assert record["output_token_ids"] == [19, 151645]
assert record["output_token_count"] == 2
assert record["hit_token_limit"] is False
assert record["generation_config"] == _FAKE_ANSWER["generation_config"]
assert record["frame_timestamps_seconds"] == [0.0, 1.0, 2.0]
assert record["frame_indices"] == [0, 30, 60]
assert record["video_path"] == _FAKE_FRAME_INFO["video_path"]
assert record["device"] == "cuda"
assert record["dtype"] == "bfloat16"
assert record["library_versions"] == _FAKE_ANSWER["library_versions"]
assert record["vision_input_shapes"] == {"pixel_values": [512, 1536]}
assert record["generation_seconds"] == 1.234
assert record["metric"] == "MRA:.5:.95:.05"
assert record["score"] == 1.0
assert record["scene"] == "scene0001_00"
assert record["question_id"] == 7
def test_write_question_result_writes_one_json_file_per_question(tmp_path):
path, record = harness_run.write_question_result(
_FAKE_ROW,
"full prompt text",
_FAKE_ANSWER,
"MRA:.5:.95:.05",
1.0,
"qwen3.5-4b",
"/root/models/qwen3.5-4b",
_FAKE_FRAME_INFO,
results_dir=tmp_path,
)
assert path == tmp_path / "scene0001_00" / "7.json"
on_disk = json.loads(path.read_text())
assert on_disk == record
def test_build_record_defaults_reasoning_fields_when_not_extended():
record = harness_run._build_record(
_FAKE_ROW,
"full prompt text",
_FAKE_ANSWER,
"MRA:.5:.95:.05",
1.0,
"qwen3.5-4b",
"/root/models/qwen3.5-4b",
_FAKE_FRAME_INFO,
)
assert record["reasoning_text"] is None
assert record["forced"] is False
assert record["forced_input_token_count"] is None
def test_build_record_carries_reasoning_fields_when_extended():
extended_answer = {
**_FAKE_ANSWER,
"reasoning_text": "long reasoning about the scene",
"reasoning_raw": "long reasoning about the scene<|im_end|>",
"reasoning_token_ids": list(range(50)),
"reasoning_token_count": 50,
"reasoning_hit_limit": True,
"forced": True,
"forced_input_token_count": 2510,
}
record = harness_run._build_record(
_FAKE_ROW,
"full prompt text",
extended_answer,
"MRA:.5:.95:.05",
1.0,
"qwen3.5-4b",
"/root/models/qwen3.5-4b",
_FAKE_FRAME_INFO,
)
assert record["reasoning_text"] == "long reasoning about the scene"
assert record["reasoning_raw"] == "long reasoning about the scene<|im_end|>"
assert record["reasoning_token_ids"] == list(range(50))
assert record["reasoning_token_count"] == 50
assert record["reasoning_hit_limit"] is True
assert record["forced"] is True
assert record["forced_input_token_count"] == 2510
def test_video_results_use_video_branch():
assert (
harness_run.results_dir_for("qwen3.5-4b", "thinking", "video", None)
== A.RESULTS_DIR / "qwen3.5-4b" / "video"
)
def test_video_record_has_no_frame_count_in_condition():
info = dict(_FAKE_FRAME_INFO, frame_selection="video", frame_count=None)
record = harness_run._build_record(
_FAKE_ROW, "prompt", _FAKE_ANSWER, "metric", 1.0, "qwen3.5-4b", "/model", info
)
assert record["condition"] == "base:video"
assert record["frame_count"] is None
|